{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day01 认识`matplotlib`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `matplotlib`是什么？\n",
    "\n",
    "　　`matplotlib`是`Python`生态下，用于绘制创造静态、动态或交互式可视化作品的第三方库，特点是操作自由度非常高，使得我们对其主要内容掌握之后，理论上可以绘制出可以想象到的任何形式的数据可视化作品。\n",
    "\n",
    "<img src='imgs/图1.png'></img>\n",
    "\n",
    "　　使用`pip install matplotlib -U`来安装最新稳定版的`matplotlib`，本教程使用的是`3.3.1`版本，你可以在安装之后运行下面的代码来查看版本信息："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-12T09:25:40.370081Z",
     "iopub.status.busy": "2020-10-12T09:25:40.370081Z",
     "iopub.status.idle": "2020-10-12T09:25:40.521711Z",
     "shell.execute_reply": "2020-10-12T09:25:40.521711Z",
     "shell.execute_reply.started": "2020-10-12T09:25:40.370081Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.0.3'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 安装完成后查看matplotlib版本\n",
    "\n",
    "import matplotlib;matplotlib.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　下面我们来看第一个非常简单的例子，作为我们之后学习`matplotlib`的**Hello World**小例子（作为`matplotlib`系列打卡活动的第一期，你不需要着急弄明白下面的代码各自在做什么）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-12T09:25:40.522672Z",
     "iopub.status.busy": "2020-10-12T09:25:40.522672Z",
     "iopub.status.idle": "2020-10-12T09:25:40.823866Z",
     "shell.execute_reply": "2020-10-12T09:25:40.823866Z",
     "shell.execute_reply.started": "2020-10-12T09:25:40.522672Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.arange(0, 10)\n",
    "y = np.arange(0, 10)\n",
    "\n",
    "# 创建图床\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "# 绘制x-y折线\n",
    "plt.plot(x, y)\n",
    "# 绘制x-y散点\n",
    "plt.scatter(x, 9-y)\n",
    "\n",
    "# 添加x轴标签\n",
    "plt.xlabel('x')\n",
    "# 添加y轴标签\n",
    "plt.ylabel('y')\n",
    "\n",
    "# 添加标题\n",
    "plt.title('Hello! matplotlib!');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　在之后日程的打卡学习活动中，我们将在除去**周五**和**周日**的其他日期里，每晚更新推进一期关于`matplotlib`系统性学习的一部分知识，并附上课后小测验，希望大家踊跃参与，努力掌握这个数据可视化利器~"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
